检索结果-内蒙古大学图书馆

Goal-Oriented Visual semantic Navigation Using semantic knowledge graph and Transformer

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 1647-1657页

作者： Wang, Zhongli Tian, Guohui Shandong Univ Sch Control Sci & Engn Jinan 250014 Peoples R China

When determining navigation actions, it is important to design effective visual and semantic representations of the observation scenes and robust navigation strategies. The paper proposes a goal-oriented visual semantic navigation method using semantic knowledge graph and transformer. Two kinds of knowledge graphs representing the location relationship between objects are constructed, namely current knowledge graph and prior knowledge graph. The pre-constructed prior knowledge graph is periodically updated by the current knowledge graph obtained in real time, and embedded into the semantic feature vector through graph convolutional network (GCN). The semantic features and extracted scene features are jointly embedded and stored, they are jointly fed into the transformer module to explore the spatio-temporal dependencies between objects in the environment. The navigation strategy is obtained from the Asynchronous Advantage Actor-Critic (A3C) model composed of Long-Short Term Memory (LSTM) and Multi-Layer Perception (MLP). Experiments show that the knowledge graph can significantly improve the navigation performance. More importantly, our experimental results show that our method can improve the generalization ability of navigation across novel scenes and novel objects. Video can be available at https://***/ZMjNvoK2rbY. Note to Practitioners-The motivation of this work is to develop an efficient visual semantic navigation method. Conventional navigation algorithms lack semantic information and learning ability, and can not adapt to the complex unknown environments. When semantic information is included in navigation, the location relationship between objects can be obtained as a prior knowledge, which can be combined with reinforcement learning to achieve autonomous navigation of agents. In this article, a knowledge graph representing the location relationships between objects has been constructed and regularly updated in real-time. The proposed visual semantic n

关键词： Visual semantic navigation semantic knowledge graph transformer reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

semantic knowledge graph Generator and Ontology Recommender: Industry Use Cases

引用

INTERNATIONAL JOURNAL OF semantic COMPUTING 2024年第2期18卷 223-238页

作者： Ferguson, Holly Yu, Dan Kritzler, Mareike Siemens Technol 1936 Univ Ave Berkeley CA 94704 USA Mentor Graph Inc 46871 Bayside Pkwy Fremont CA 94538 USA

Incorporating semantics in modern projects can be time-consuming, rely on limited re-sources, and require expert knowledge to implement in a future-proof manner. Across many working groups and industrial fields, semantics continues to help solve data consolidation challenges;however, there are still emerging situations such as where modeling and ontology engineering efforts overlap, ongoing siloed data, or even continuing to create tech-debt for the future. Tools have been developed and researched that offer users assistance with the automation of select expert tasks to help streamline semantic artifact creation, understanding of design decisions, and domain data alignments. The semantic knowledge graph Generator and Recommendation Framework encourage semantic projects that are automated from the start to follow industry standards, enhanced with recommendations, and use components which make semantic artifacts more accessible for non-semantic experts. Based on that tool, this extension article presents several industry use cases and related discussion where semantic recommendations and deployment automation have assisted with project development, teaching about ontology structures, and gaining accessibility of semantic artifacts.

关键词： semantic project automation semantic recommender ontology recommender semantic knowledge graph semantic recommendation ontology education semantics education semantic decision support ontology accessibility adoption of semantics semantic case study, industry semantics

来源：评论

学校读者我要写书评

暂无评论

Hierarchical aspect-based sentiment analysis using semantic capsuled multi-granular networks

引用

INFORMATION SYSTEMS 2025年 132卷

作者： Gracewell, Jeffin Raj, A. Arul Edwin Kalaivani, C. T. Renugadevi, R. Saveetha Engn Coll Chennai Tamil Nadu India Univ Tun Hussein Onn Parit Raja Malaysia Vignans Fdn Sci Technol & Res Guntur Andhra Pradesh India

In the ever-evolving domain of sentiment analysis, discerning intricate sentiments towards specific aspects and their sub-components within textual data has become pivotal. This paper introduces the semantic Capsuled Hierarchical Multi-Granular Network (SCH-MGN) model, an innovative approach explicitly designed for aspect-based sentiment analysis (ABSA) challenges. The SCH-MGN model is primed to evaluate sentiments at both macro (broader topics) and micro (detailed sub-aspects) hierarchical levels, offering a comprehensive sentiment evaluation spectrum. By integrating mechanisms like the semantic knowledge graph Attention Network (SKGAN) for targeted aspect extraction, Hierarchical Embedding Layers leveraging Multilingual BERT (mBERT), and advanced neural architectures including Recurrent Neural Networks (RNNs) and Temporal Convolutional Networks (TCNs), the model ensures a nuanced sentiment interpretation. The paper provides a meticulous dissection of the model's methodology, from tokenization and embedding to detailed sentiment extraction, accentuating its capability to offer granular sentiment interpretations. Empirical illustrations validate the model's proficiency in handling compound sentiments, cementing its potential as an indispensable tool for businesses, reviewers, and analysts. This groundbreaking approach to ABSA promises to redefine the granularity with which we understand and evaluate textual sentiments in diverse domains.

关键词： Aspect-based sentiment analysis Hierarchical sentiment evaluation semantic knowledge graph Capsule networks BERT embeddings Context preservation

来源：评论

学校读者我要写书评

暂无评论

graph-Based Visual-semantic Entanglement Network for Zero-Shot Image Recognition

引用

IEEE TRANSACTIONS ON MULTIMEDIA 2022年 24卷 2473-2487页

作者： Hu, Yang Wen, Guihua Chapman, Adriane Yang, Pei Luo, Mingnan Xu, Yingxue Dai, Dan Hall, Wendy South China Univ Technol Guangzhou 510006 Peoples R China Univ Southampton Southampton SO17 1BJ Hants England Univ Lincoln Lincoln LN6 7TS England

Zero-shot learning uses semantic attributes to connect the search space of unseen objects. In recent years, although the deep convolutional network brings powerful visual modeling capabilities to the ZSL task, its visual features have severe pattern inertia and lack of representation of semantic relationships, which leads to severe bias and ambiguity. In response to this, we propose the graph-based Visual-semantic Entanglement Network to conduct graph modeling of visual features, which is mapped to semantic attributes by using a knowledge graph, it contains several novel designs: 1. it establishes a multi-path entangled network with the convolutional neural network (CNN) and the graph convolutional network (GCN), which input the visual features from CNN to GCN to model the implicit semantic relations, then GCN feedback the graph modeled information to CNN features;2. it uses attribute word vectors as the target for the graph semantic modeling of GCN, which forms a self-consistent regression for graph modeling and supervise GCN to learn more personalized attribute relations;3. it fuses and supplements the hierarchical visual-semantic features refined by graph modeling into visual embedding. Our method outperforms state-of-the-art approaches on multiple representative ZSL datasets: AwA2, CUB, and SUN by promoting the semantic linkage modelling of visual features.

关键词： Visualization semantics Pipelines knowledge engineering Load modeling Task analysis Couplings Zero-shot learning visual-semantic modeling graph convolutional network semantic knowledge graph attribute word embedding

来源：评论

学校读者我要写书评

暂无评论

Remote Sensing Image Interpretation With semantic graph-Based Methods: A Survey

引用

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 4544-4558页

作者： Sun, Shengtao Dustdar, Schahram Ranjan, Rajiv Morgan, Graham Dong, Yusen Wang, Lizhe Yanshan Univ Dept Comp Sci & Engn Qinhuangdao 066004 Hebei Peoples R China TU Wien Comp Sci A-1040 Vienna Austria Newcastle Univ Sch Comp Sci Newcastle Upon Tyne NE1 7RU Tyne & Wear England China Univ Geosci Fac Earth Sci Wuhan 430079 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430079 Peoples R China

With the significant improvements in Earth observation (EO) technologies, remote sensing (RS) data exhibit the typical characteristics of Big Data. Propelled by the powerful feature extraction capabilities of intelligent algorithms, RS image interpretation has drawn remarkable attention and achieved progress. However, the semantic relationship and domain knowledge hidden in massive RS images have not been fully exploited. To the best of our knowledge, a comprehensive review of recent achievements regarding semantic graph-based methods for comprehension and interpretation of RS images is still lacking. Specifically, this article discusses the main challenges of RS image interpretation and presents a systematic survey of typical semantic graph-based methodologies for RS knowledge representation and understanding, including the Ontology Model, Geo-Information Tupu, and semantic knowledge graph. Furthermore, we categorize and summarize how the existing technologies address different challenges in RS image interpretation based on semantic graph-based methods, which indicates that the semantic information about potential relationships and prior knowledge of variant RS targets are central to the solution. In addition, a case study of RS geological interpretation based on the semantic knowledge graph is demonstrated to show the promising capability of intelligent RS image interpretation. Finally, the future directions are discussed for further research.

关键词： semantics Remote sensing Ontologies Big Data Data models Earth Data mining Remote sensing (RS) image interpretation remotes sensing big data RS geological interpretation semantic graph-based method semantic knowledge graph

来源：评论

学校读者我要写书评

暂无评论

QBEES: query-by-example entity search in semantic knowledge graphs based on maximal aspects, diversity-awareness and relaxation

引用

JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2017年第3期49卷 333-366页

作者： Metzger, Steffen Schenkel, Ralf Sydow, Marcin Max Planck Inst Informat Saarbrucken Germany Univ Trier Trier Germany Polish Acad Sci Inst Comp Sci Warsaw Poland Polish Japanese Acad Informat Technol Warsaw Poland

Structured knowledge bases are an increasingly important way for storing and retrieving information. Within such knowledge bases, an important search task is finding similar entities based on one or more example entities. We present QBEES, a novel framework for defining entity similarity based on structural features, so-called aspects and maximal aspects of the entities, that naturally model potential interest profiles of a user submitting an ambiguous query. Our approach based on maximal aspects provides natural diversity awareness and includes query-dependent and query-independent entity ranking components. We present evaluation results with a number of existing entity list completion benchmarks, comparing to several state-of-the-art baselines.

关键词： semantic knowledge graph Similar entity search Aspect model Diversity Experimental evaluation

来源：评论

学校读者我要写书评

暂无评论

The notion of diversity in graphical entity summarisation on semantic knowledge graphs

引用

JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2013年第2期41卷 109-149页

作者： Sydow, Marcin Pikula, Mariusz Schenkel, Ralf Polish Japanese Inst Informat Technol Web Min Lab Warsaw Poland Polish Acad Sci Inst Comp Sci PL-00901 Warsaw Poland Univ Saarland D-66123 Saarbrucken Germany MPI Informat Saarbrucken Germany

Given an entity represented by a single node q in semantic knowledge graph D, the graphical Entity Summarisation problem (GES) consists in selecting out of D a very small surrounding graph S that constitutes a generic summary of the information concerning the entity q with given limit on size of S. This article concerns the role of diversity in this quite novel problem. It gives an overview of the diversity concept in information retrieval, and proposes how to adapt it to GES. A measure of diversity for GES, called ALC, is defined and two algorithms presented, baseline, diversity-oblivious PRECIS and diversity-aware DIVERSUM. A reported experiment shows that DIVERSUM actually achieves higher values of the ALC diversity measure than PRECIS. Next, an objective evaluation experiment demonstrates that diversity-aware algorithm is superior to the diversity-oblivious one in terms of fact selection. More precisely, DIVERSUM clearly achieves higher recall than PRECIS on ground truth reference entity summaries extracted from Wikipedia. We also report another intrinsic experiment, in which the output of diversity-aware algorithm is significantly preferred by human expert evaluators. Importantly, the user feedback clearly indicates that the notion of diversity is the key reason for the preference. In addition, the experiment is repeated twice on an anonymous sample of broad population of Internet users by means of a crowd-sourcing platform, that further confirms the results mentioned above.

关键词： Diversity semantic knowledge graph graphical entity summarisation Summarisation Diversity measure Algorithm Experiment Crowdsourcing

来源：评论

学校读者我要写书评

暂无评论

引用

IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (WI-IAT)

作者： Metzger, Steffen Schenkel, Ralf Sydow, Marcin Max Planck Inst Informat Saarbrucken Germany Univ Passau Passau Germany Polish Acad Sci Inst Comp Sci PL-00901 Warsaw Poland Polish Japanese Inst Informat Technol Warsaw Poland

ISBN: (纸本)9781479941438

Structured knowledge bases are an increasingly important way for storing and retrieving information. Within such knowledge bases, an important search task is finding similar entities based on one or more example entities. We present QBEES, a novel framework for defining entity similarity based only on structural features, so-called aspects, of the entities, that naturally model potential interest profiles of a user submitting an ambiguous query. The aspect model provides natural diversity-awareness and includes query-dependent and query-independent entity ranking components. We present evaluation results with a number of existing entity list completion benchmarks, comparing to several state-of-the-art baselines.

关键词： graph theory information storage knowledge based systems query processing QBEES ambiguous query aspect-based similar entity search diversity-awareness entity list completion benchmark entity similarity information retrieval query-dependent entity ranking component query-independent entity ranking component relaxation search task semantic knowledge graph similar entity finding structural features structured knowledge base user potential interest profiles Compounds Computational modeling Context Feature extraction Motion pictures Ontologies semantics ontology semantic data semantic similarity Data Storage information retrieval Ontology Cinema knowledge based systems knowledge Bases semantics query analysis Computational modeling graph theory Structural properties Feature extraction relaxation Relaxation Films

来源：评论

学校读者我要写书评

暂无评论

Description of Open Data Sets as semantic knowledge graphs to Contribute to Actions Related to the 2030 Agenda and the Sustainable Development Goals 1

引用

2nd Iberoamerican knowledge graphs and semantic Web Conference (KGSWC) / 1st Indo-American knowledge graphs and semantic Web Conference

作者： Eguiguren, Jose Piedra, Nelson Univ Tecn Particular Loja Loja Ecuador

ISBN: (数字)9783030653842

ISBN: (纸本)9783030653842;9783030653835

This work aims to find links between Open Data (OD) and the Sustainable Development Goals (SDG). There is a significant amount of OD published by different organizations from multiple areas of knowledge. However, this data is dispersed in different databases around the world, and it is highly heterogeneous. Consequently, hindering the ability to leverage these vast amounts of information that may contribute to the progress of the SDGs. The Sustainable Development Goals are structured in natural language, limiting the extraction of useful information. This work proposes and describes an architecture that allows the integration between the SDGs and OD through dynamically materializing nodes and edges in the form of a semantic knowledge graph representation built automatically from information stored within repositories of open data sets related to the 2030 agenda and the SDGs domain. The resulting graph represents a collection of interlinked descriptions of named entities and properties that contribute to one another, forming a semantic graph, where each node and edges represents part of the description of the entities, related to it. Doing so may provide better ways to achieve and track the Sustainable Development Goals with already existing information.

关键词： Sustainable Development Goals semantic knowledge graph semantic Web Linked open data semantic integration

来源：评论

学校读者我要写书评

暂无评论

Modeling Recommendation and Project Deployment Automation for semantic knowledge graphs 17

Modeling Recommendation and Project Deployment Automation fo...

引用

17th IEEE International Conference on semantic Computing (ICSC)

作者： Ferguson, Holly Yu, Dan Kritzler, Mareike Siemens Smart Machine Vision Team Berkeley CA USA Mentor Graph Corp Integrated Circuits Verificat Berkeley CA USA

ISBN: (纸本)9781665482639

semantic technology continues to advance but adoption still has existing and emerging challenges due to varying amounts of individual knowledge for performing development tasks, limited semantic-expert resources, as well as fast-paced time lines to produce industry products. semantic modeling is usually time-consuming even for those with the expertise. Ad-hoc and rushed implementations may result in less ideal interoperability in downstream work, slowing down adoption of knowledge graphs in intelligent systems. The objective of this contribution is to use automation of certain expert tasks to propel semantic development efforts toward solutions that avoid compounding semantic tech debt for data alignment-based tasks and subsequent knowledge graph-based projects. This work aims to function in tandem with other existing internal data curation and deployment efforts to offer an automation-based framework and tool with results automatically deployable to a chosen project space, semantic editor, or triple store. This ultimately encourages development with artifacts that have already been assembled with all semantic dependencies to encourage using them alongside other industry standards. It also reduces expensive and redundant modeling efforts, helps to teach non-semantic experts about the implications of their modeling choices with recommendations and paradigm delineations, and eases remaining data alignment work so that results more easily follow semantic best practices.

关键词： semantic project automation semantic knowledge graph recommender ontology recommender semantic knowledge graph ontology education tool semantic graph education tool semantic knowledge graph decision support ontology accessibility industry adoption of semantics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：